Testing marine data assimilation in the northeastern Baltic using satellite SST products from the Copernicus Marine Environment Monitoring Service

Zujev, Mihhail; Elken, Jüri

doi:10.3176/proc.2018.3.03

Cited by 8 publications

(13 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to the EOF reconstruction, OI was used for comparison purposes in two configurations: (i) interpolation of deviations from locally resolved mean (modeled) fields that includes high gradients in the coastal areas of river influence zones, (ii) interpolation of deviations from smooth climatological mean fields. Both configurations used Gaussian correlation function in the form C (r) = exp(−r 2 /R 2 ) (e.g., Zujev and Elken, 2018), where r is the space lag and R is the correlation scale. The OI configurations used for smoothing purposes prescribed noise-to-signal ratio η 2 .…”

Section: Estimation Of Reconstruction Accuracy Using Pseudo-observationsmentioning

confidence: 99%

“…The present paper has been initially motivated by the need of detailed examination of spatial covariance characteristics in a specific region -the northeastern Baltic, in relation to the data assimilation. Although using OI with Gaussian correlation function provided satisfactory results (Zujev and Elken, 2018), need for improved description of statistics deemed obvious. During different test options, we used also traditional EOF method.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reconstruction of Large-Scale Sea Surface Temperature and Salinity Fields Using Sub-Regional EOF Patterns From Models

et al. 2019

View full text Add to dashboard Cite

A method for reconstruction of gridded fields of sea surface variables from timedependent observations, using sub-regional EOF (Empirical Orthogonal Functions) patterns from models, is presented and tested. Covariance fields, calculated from the model results over long enough time span, are used to find EOF modes. The gravest "observational" amplitudes and their first temporal derivatives are determined from the least-square minimization of fitting errors in relation to the observed values. The field is reconstructed by superposition of continuous model-based mode patterns multiplied by observational amplitudes that meet adopted statistical limits. If the observational amplitude exceeds the limits, gridded fields for this and higher modes are not produced. We applied the method in the northeastern Baltic over the model time series 2010-2015. Daily averages of sea surface temperature (SST) and salinity (SSS) from the highresolution (grid step 0.5 nautical miles) sub-regional HBM model were spatially averaged over bins of 5 × 5 nautical miles. Three first modes cover 99% of variance of temperature and 61.4% of salinity. As shown by experiments with pseudo-observations (model values at these points reconstructed to the model grid and then compared with the original model data), reconstruction performance depends on the configuration of the observation points in the model domain. Still, a few first modes usually produce acceptable results. When removing the SST seasonal cycle prior to EOF analysis, spatial patterns of leading modes remained practically unchanged, share of variance of the three first modes was reduced to 88.6% and reconstruction errors were reduced by about 25%. Sufficient spatial data coverage of the larger basin with ship-born observations usually takes quite long time-of the order of month; therefore, time correction of the amplitudes using the found temporal derivatives improves the accuracy of reconstruction. The method is compared with the Optimal Interpolation (OI) by using the pseudo-observations. Results show that, for SST reconstruction, the OI method is significantly worse than the EOF method. For SSS, OI is slightly better than EOF. The superiority of EOF is that the remote correlation patterns can be used in the reconstruction, which is important when the observations are sparse.

show abstract

Section: Estimation Of Reconstruction Accuracy Using Pseudo-observationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reconstruction of Large-Scale Sea Surface Temperature and Salinity Fields Using Sub-Regional EOF Patterns From Models

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The major advantage of such data sets is a possibility to record, with a high resolution, temperature fields over large areas; another advantage is the rapidity with which such data are available. The major disadvantage of remotely sensed SST, especially in the Baltic Sea region, is the low accuracy on the order of 1 • C [40,58]. Another problem stems from the fact that the measurement pertains only to a very thin surface water layer.…”

Section: Filtration and Assimilation Of Sst Satellite Data From Avhrrmentioning

confidence: 99%

“…In addition, the satellite images are uniformly distributed over the rectangular spatial grid. Therefore, in this case, simple, numerically effective methods are successful, e.g., the Cressman method [58,70]. The basic problem in this case is, however, the low accuracy of the bulk surface temperature computed from the observed satellite skin temperature.…”

Section: Filtration and Assimilation Of Sst Satellite Data From Avhrrmentioning

confidence: 99%

“…The basic problem in this case is, however, the low accuracy of the bulk surface temperature computed from the observed satellite skin temperature. Although the root-mean-square error (RMSE) is usually in the Baltic Sea on the order of 1 • C, in some cases, e.g., if the cloud has not been masked in a given pixel, it may be as high as 10 • C [58]. Therefore, an important part of the assimilation algorithm is an appropriate preliminary filtration of the data.…”

Section: Filtration and Assimilation Of Sst Satellite Data From Avhrrmentioning

confidence: 99%

See 1 more Smart Citation

Combining Satellite Imagery and Numerical Modelling to Study the Occurrence of Warm Upwellings in the Southern Baltic Sea in Winter

Kowalewska-Kalkowska

Kowalewski

2019

Remote Sensing

View full text Add to dashboard Cite

Coastal upwelling involves an upward movement of deeper, usually colder, water to the surface. Satellite sea surface temperature (SST) observations and simulations with a hydrodynamic model show, however, that the coastal upwelling in the Baltic Sea in winter can bring warmer water to the surface. In this study, the satellite SST data collected by the advanced very high resolution radiometer (AVHRR) and the moderate-resolution imaging spectroradiometer (MODIS), as well as simulations with the Parallel Model 3D (PM3D) were used to identify upwelling events in the southern Baltic Sea during the 2010-2017 winter seasons. The PM3D is a three-dimensional hydrodynamic model of the Baltic Sea developed at the Institute of Oceanography, University of Gdańsk, Poland, in which parallel calculations enable high-resolution modelling. A validation of the model results with in situ observations and satellite-derived SST data showed the PM3D to adequately represent thermal conditions in upwelling areas in winter (91.5% agreement). Analysis of the frequency of warm upwellings in 12 areas of the southern Baltic Sea showed a high variability in January and February. In those months, the upwelling was most frequent, both in satellite imagery and in model results, off the Hel Peninsula (38% and 43% frequency, respectively). Upwelling was also frequent off the Vistula Spit, west of the Island of Rügen, and off the eastern coast of Skåne, where the upwelling frequency estimated from satellite images exceeded 26%. As determined by the PM3D, the upwelling frequency off VS and R was at least 25%, while off the eastern coast of Skåne, it reached 17%. The faithful simulation of SST variability in the winters of 2010-2017 by the high-resolution model used was shown to be a reliable tool with which to identify warm upwellings in the southern Baltic Sea.

show abstract

Reconstructing sea surface temperature and salinity fields in the northeastern Baltic from observational data, based on sub-regional Empirical Orthogonal Function (EOF) patterns from models

Elken

Zujev

Lagemaa

2018

2018 IEEE/OES Baltic International Symposium (BALTIC)

View full text Add to dashboard Cite

Testing marine data assimilation in the northeastern Baltic using satellite SST products from the Copernicus Marine Environment Monitoring Service

Cited by 8 publications

References 0 publications

Reconstruction of Large-Scale Sea Surface Temperature and Salinity Fields Using Sub-Regional EOF Patterns From Models

Reconstruction of Large-Scale Sea Surface Temperature and Salinity Fields Using Sub-Regional EOF Patterns From Models

Combining Satellite Imagery and Numerical Modelling to Study the Occurrence of Warm Upwellings in the Southern Baltic Sea in Winter

Reconstructing sea surface temperature and salinity fields in the northeastern Baltic from observational data, based on sub-regional Empirical Orthogonal Function (EOF) patterns from models

Contact Info

Product

Resources

About